It will also store server paths under the "wildfly_config" metric family with the label name identifying the path such as "jboss_home_dir" or "jboss_server_config_dir" and the label value being the path itself.

And of course the default behavior of the JMX Exporter is to also emit metrics for the JVM statistics as well (such as heap and non-heap memory usage) so we get those "for free".

A further interesting integration can be the addition of Hawkular Alerts to the environment.

As the previous blog and demo discuss, Hawkular Alerts is a generic, federated alerts system that can trigger events, alerts, and notifications from different, independent systems such as Prometheus, ElasticSearch, and Kafka.

Here we can combine the two. Let's follow the directions for the OpenTracing demo (using the Jaeger implementation) and add Hawkular Alerts.

What this can show is OpenTracing application metrics triggering alerts when (as in this example) OpenTracing spans encounter a larger-than-expected error rates.

(Note: these instructions assume you are using Kubernetes / Minikube - see the Hawkular OpenTracing blogs linked above for more details on these instructions)

1. START KUBERNETES

Here we start minikube giving it enough resources to run all of the pods necessary for this demo. We also start up a browser pointing to the Kubernetes dashboard, so you can follow the progress of the remaining instructions.

Now navigate back to the “Dashboard” page (again via the left-hand nav menu). From this Dashboard page, look for alerts when they are triggered. We'll next start generating the data that will trigger these alerts.

Notice the “ordermgr” service (version "0.0.1") had an error rate of 0.3333 (33%) which caused the alert since it is above the allowed 30% threshold.

At this point, the Hawkular Alerts UI provides the ability for system admins to log notes about the issue, acknowledge the alert and mark the alert resolved if the underlying issue has been fixed. These lifecycle functions (also available as REST operations) are just part of the value add of Hawkular-Alerts.

You could do more complex things such as only trigger this alert if this Prometheus query generated results AND some other condition was true (say, ElasticSearch logs match a particular pattern, or if a Kafka topic had certain data). This demo merely scratches the surface, but does show how Hawkular Alerts can be used to work with OpenTracing to provide additional capabilities that may be found useful by system administrators and IT support personnel.

Friday, August 11, 2017

Federated Alerts

Hawkular Alerts aims to be a federated alerting system. That is to say, it can fire alerts and send notifications that are triggered by data coming from a number of third-party external systems.

Thus, Hawkular Alerts is more than just an alerting system for use with Hawkular Metrics. In fact, Hawkular Alerts can be used independently of Hawkular Metrics. This means you do not even have to be using Hawkular Metrics to take advantage of the functionality provided by Hawkular Alerts.

This is a key differentiator between Hawkular Alerts and other alerting systems. Most alerting systems only alert on data coming from their respective storage systems (e.g. the Prometheus Alert Engine alerts only on Prometheus data). Hawkular Alerts, on the other hand, can trigger alerts based on data from various systems.

Alerts vs. Events

Before we begin, a quick clarification is in order. When it is said that Hawkular Alerts fires an "alert" it means some data came into Hawkular Alerts that matched some conditions which triggered the creation of an alert in Hawkular Alerts backend storage (which can then trigger additional actions such as sending emails or calling a webhook). An "alert" typically refers to a problem that has been detected, and someone should take action to fix it. An alert has a lifecycle attached to it - alerts are opened, then acknowledged by some user who will hopefully fix the problem, then resolved when the problem can be considered closed.

However, there can be conditions that occur that do not represent problems but nevertheless are events you want recorded. There is no lifecycle associated with events and no additional actions are triggered by events, but "events" are fired by Hawkular Alerts in the same general manner as "alerts" are.

In this document, when it is said that Hawkular Alerts can fire "alerts" based on data coming from external third-party systems such as Prometheus, ElasticSearch, and Kakfa, this also means events can be fired as well as alerts. What this means is you can record any event (not just a "problem", aka "alert") that can be gleaned from this data coming from external third-party systems.

Demo

There is a recorded demo found here that will illustrate what this document is describing. After you read this document, you should watch the demo to gain further clarity on what is being explained. The demo is the multiple-sources example which you can run yourself found here (note: at the time of writing, this example is only found in the next branch, to be merged in master soon).

Prometheus

Hawkular Alerts can take the results of Prometheus metric queries and use the queried data for triggers that can fire alerts.

This Hawkular Alerts trigger will fire an alert (and send an email) when a Prometheus metric indicates our store’s inventory of widgets is consistently low (as defined by the Prometheus query you see in the "expression" field of the condition):

Integration with Prometheus Alert Engine

As a side note, though not demostrated in the example, Hawkular Alerts also has an integration with Prometheus' own Alert Engine. This means the alerts generated by Prometheus itself can be forward to Hawkular Alerts which can, in turn, be used for additional processing, perhaps for use with data that is unavailable to Prometheus that can tell Hawkular Alerts to fire other alerts. For example, Hawkular Alerts can take Prometheus alerts as input and feed it back into other conditions that trigger on the Prometheus alert along with ElasticSearch logs.

ElasticSearch

Hawkular Alerts can examine logs stored in ElasticSearch and trigger alerts based on patterns that match within the ElasticSearch log messages.

This Hawkular Alerts trigger will fire an alert (and send an email) when ElasticSearch logs indicate sales are being lost due to inventory being out of stock of items (as defined by the condition which looks for a log category of "FATAL" which happens to mean a lost sale in the case of the store’s logs). Notice dampening is enabled on this trigger - this alert will only fire when the logs indicate lost sales every 3 times.

Kafka

This Hawkular Alerts trigger will fire an alert when data over a Kakfa topic indicates a large purchase was made to fill the store’s inventory (as defined by the condition which evaluates to true when any number over 17 is received on the Kafka topic):

But, Wait! There’s More!

The above only mentions the different ways Hawkular Metrics retrieves data for use in determining what alerts to fire. What is not covered here is the fact that Hawkular Alerts can stream data in the other direction as well - Hawkular Alerts can send alert and event data to things like an ElasticSearch server or a Kafka broker. There are additional examples (mentioned below) that can demonstrate this capability.

The point is Hawkular Alerts should be seen as a shared, common alerting engine that can be shared for use by multiple third-party systems and can be used as both a consumer and producer - as a consumer of the data from external third-party systems (which is used to fire alerts and events) and as a producer to send notifications of alerts and events to external third-party systems.

More Examples

Take a look at the Hawkular Alerts examples for more examples on using external systems as data to be used for triggering alerts. (note: at the time of writing, some examples are currently in the next branch such as the Kafka ones).

Thursday, October 20, 2016

It is implemented in Go and the main use case for which it was created is to be able to collect metrics from OpenShift pods. The idea is you run Hawkular OpenShift Agent (HOSA) on an OpenShift node and HOSA will listen for pods to come up and down on the node. As pods come online, the pods will tell the agent what (if any) metrics should be collected. As pods go down, the agent will stop collecting metrics from all endpoints running on that pod.

Today, only Prometheus endpoints (using either the binary or text protocol) can be scraped with Jolokia endpoints next on the list to be implemented. So HOSA will be able to support collecting metrics from either type of endpoint in the near future.